Quantum Machine Learning: Geometric Mapping for Optimal Parameter Optimization

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers a paradigm shift in computation, potentially unlocking unprecedented capabilities in areas like drug discovery, materials science, and optimization. This paper investigates the application of geometric mapping to QML, specifically focusing on optimizing quantum model parameters through the application of geometric transformations. We propose a novel framework that utilizes geometric properties to guide the parameter search process, leading to improved model accuracy and efficiency. This research explores the benefits of this approach compared to traditional machine learning techniques, particularly in the context of quantum systems. The core mechanism involves mapping a quantum system's state space to a geometric space, leveraging geometric properties to enhance the optimization process. The paper concludes with a discussion of the potential impact of this geometric mapping technique on the future of QML.

DOI: 10.5281/zenodo.22322202

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